This example is very similar to examples in the lecture notes in the first year animal behaviour module (ACE1027). Worked Example - Two Sample Paired t-test (This is for a two tailed or paired t-test, for a one-tailed t-test the probabilities are halved-see worked example below). Here is an example of a t-table with explanations of what each bit means. Minitab and R also can be used to test for normality. We use F-tests (usually in Minitab or R) to check our two samples have equal variances. The main difference between these two tests is that the t statistic is calculated differently (using differences for Paired), however Minitab and R calculate this for you, once you specify which type of two sample t-test you would like to perform. Independent/unrelated t-test - there is no link between the groups (different independent groups).Paired t-test/related t-test - comparing two sets of results which are linked (where you test the same group of participants twice or your two groups are similar).We shall look at two types of two sample t-tests: Now we calculate the test statistic using the formula below.Ī two sample t-test compares two samples of normally distributed data where the population variance is unknown and the sample sizes are small ($n \lt 30$).We must first identify the null and alternative hypothesis.However, you may also be introduced to how to conduct and interpret hypothesis test without using such software (this is good to demonstrate a thorough knowledge of what is really happening with the data). The MethodĪs you progress through your university career you will be introduced to statistical packages such as R and Minitab that can perform these tests for you and present the final significance level. For example, we might want to test whether the proportion of red squirrels to grey squirrels in Newcastle is different from the known UK average. We want to test the null hypothesis that the population mean is equal to the sample mean. is the mean of the sample the same as the known mean? A one-sample t-test is used to compare a sample mean $\bar-$ (calculated using the data) to a known ‘’population’’ mean $\mu$ (typically obtained in previous research). You want to test the null hypothesis, i.e. Usually you would compare your data with a known value, typically a mean that has been derived from previous research. This is where you are only testing one sample, for example the number of owls in an area over the past 10 years. Contents Toggle Main Menu 1 One Sample t-tests 1.1 The Method 2 Worked Example 1 3 Two Sample t-tests 4 The t-table 5 Worked Example 2 6 Worked Example 3 7 Test Yourself 8 See Also One Sample t-tests
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